Improving anxiety research: novel approach to reveal trait anxiety through summary measures of multiple states

The reliability and validity of preclinical anxiety testing is essential for translating animal research into clinical use. However, the commonly used anxiety tests lack inter-test correlations and face challenges with repeatability. While translational animal research should be able to capture stable individual anxiety traits, the current approach employes a single type of test at a single time that only measures transient states of animals, heavily influenced by experimental conditions. Here, we propose a validated, optimized test battery capable of reliably capturing trait anxiety in rats and mice of both sexes. Instead of developing novel tests, we combined widely-used tests (elevated plus-maze, open field and light-dark test) to provide instantly applicable adjustments for better predictive validity. We repeated these tests three times to capture behaviour across multiple challenges, which we combined together to generate summary measures (SuMs). Our approach resolved between-test correlation issues and provided better predictions for subsequent outcomes under anxiogenic conditions or fear conditioning. Moreover, SuMs prove more sensitive detecting anxiety differences in an etiological stress model. Finally, we tested our method’s efficiency in discovering anxiety-related molecular pathways through RNA sequencing of the medial prefrontal cortex. Using SuMs, we identified four-times more molecular correlates of trait anxiety as compared to transient anxiety states, pointing out novel functional gene clusters. Furthermore, 16% of the most robust molecular findings also correlated with anxiety in the amygdala. In summary, we provide a novel approach to capture trait anxiety in rodents, offering improved predictions for potential therapeutic targets for personalized medicine.